How generative AI can be used in the creative industries
| 7 Min Read
Generative AI is changing how creative professionals develop ideas and produce finished work across film, music, design, advertising, publishing, and games.
This article explores how generative AI is reshaping creative work, including how it functions and is being applied today, and what skills professionals need to use it effectively.
What generative AI means for creative professionals
Generative AI refers to intelligent systems that produce material, including text, images, audio, video, and code, based on patterns learned from existing data. Contrary to the idea that AI replaces human imagination, it gives people another tool to shape their creative direction. You can use generative AI to create and refine ideas faster, freeing up time to focus on the parts of the creative process that require human judgement and input.
How generative AI models work
Generative AI models are trained on large datasets. They learn statistical patterns in data and then use those patterns to produce new outputs when prompted.
Different Gen AI models specialise in various creative tasks:
- Large language models (LLMs) generate written content
- Image and video models create visuals from text prompts
- Audio models produce music, sound effects, and synthetic voices
- Code models can write and improve programming code
Since every output is built entirely from patterns learned during training, generative AI produces very different results depending on the tasks and prompts it’s given.
Generative AI use cases across the creative industries
Using generative AI in the early stages of the creative process can reduce the time spent exploring creative ideas, developing first drafts, and deciding what to develop further.
Here’s how generative AI is being used across different creative fields.
Film, television, animation, and visual effects
In film and television, generative tools help with pre-visualisation, allowing directors and designers to explore visual ideas before committing budget to production. In animation and visual effects, AI can assist with background generation and rough animation passes, which artists then refine by hand.
Music, design, advertising, publishing, and games
Outside film and television, generative AI is being applied to a broader mix of creative and production tasks.
- Music: Composers can use generative AI tools to help develop demo tracks.
- Design and advertising: Generative AI tools help teams create rapid mockups and campaign concepts for quick testing and iteration.
- Publishing: Assists with drafting and formatting manuscripts.
- Games: Produces environment assets and procedural content that would otherwise take far longer to create manually.
Across these use cases, generative AI can work as an ideation partner, branching and organising ideas while creative professionals guide the train of thought.
Benefits and risks of using AI for creative tasks
Generative AI can help you explore more sources of inspiration, including ones you might otherwise overlook or never come across. In the process, it can shorten production timelines and help teams work within their budgets.
At the same time, generative AI has its limitations, such as producing outputs that are generic or factually wrong. Additionally, over-reliance on AI without the critical thinking your work requires can limit creative growth and reduce opportunities to experiment independently.
While generative AI allows small teams to prototype at a scale previously reserved for larger studios, human review remains essential to verify that content follows copyright laws and is not plagiarised before publication.
Managing the risks of AI in creative work
Creative professionals using AI should check licensing terms before using them commercially, since copyright, ownership, and training data remain key concerns. The World Intellectual Property Organization (WIPO) continues to examine how intellectual property laws apply to AI-generated content, which is why it’s important to understand how to train AI tools before using them.
Human review is also essential, since AI can reproduce bias from its training data; limited transparency can make these issues harder to identify. The NIST AI Risk Management Framework highlights these risks, emphasising the importance of human review when using AI for creative work.
What you need to know when working with generative AI
As generative AI becomes more common in creative workflows, professionals need skills in directing AI tools with clear instructions and evaluating the outcomes AI produces.
Working with AI tools
The skills you need will depend on the work, but several apply across creative AI projects. These include:
- Prompt design: Writing clear instructions that help a model produce useful results through iteration.
- Prototyping: Using AI to explore multiple directions quickly before choosing the strongest idea.
- Critical evaluation: Assessing whether AI-generated content is original and free from copyright or bias issues.
- Refinement: Editing AI-generated content to make it more engaging and meaningful.
- Collaboration: Working across creative, technical, and strategic teams to develop and evaluate AI-assisted work.
How Goldsmiths’ online MSc builds creative AI practice
For those looking to build creative AI skills formally, a creative AI degree offers a structured way to combine technical understanding with creative judgement.
Goldsmiths’ online Artificial Intelligence and Creative Practice MSc gives you hands-on learning while developing your understanding in core areas such as prompt engineering and AI ethics.
Studio sprints and a major creative project
The Goldsmiths’ Artificial Intelligence and Creative Practice MSc runs on studio-style sprints that reflect how real creative teams work with AI. Responsible innovation runs through the curriculum, with students examining the ethical questions that arise when AI is used in creative projects.
The programme ends with a major creative project, giving students a portfolio piece that demonstrates both technical skill and creative direction to future employers.
Staying in charge of creativity in the age of AI
What sets a creative professional who uses AI apart from one who is skilled in AI is technical knowledge alongside creative judgement.
At a time when everyone has access to AI, you need to know what to ask of the technology and how to evaluate its responses. You also need to distinguish inspiration from copyright infringement and account for bias. That clarity will help you lead with your creative work as AI becomes the norm.
For those looking to build creative AI skills formally, a creative AI degree offers a structured pathway to bring technical and creative practice together.
Frequently asked questions
What is artificial intelligence?
Artificial Intelligence refers to the development of intelligent systems capable of analysing data, recognising patterns, and solving complex real-world problems.
At Goldsmiths, you’ll explore AI across three distinct MSc pathways. New for 2026, the MSc in AI and Data Analytics combines technical grounding in AI with applied data analysis skills. The MSc in AI and Machine Learning focuses on building and deploying machine learning solutions. The MSc in AI and Creative Practice teaches the application of AI as a creative medium across film, music, design, and culture.
Is AI truly creative?
At Goldsmiths, AI is treated as a creative collaborator that expands human imagination rather than replacing it. The Artificial Intelligence and Creative Practice MSc Online is built around this premise, challenging students to critically examine and shape how AI intersects with art and creative expression.
Do I need to know how to code?
No, you don’t need prior programming experience for the Artificial Intelligence and Creative Practice MSc Online. Students come from backgrounds including arts, design, media, and the humanities, so coding doesn’t have to be your starting point. The AI and Machine Learning MSc (online) and the AI and Data Analytics MSc (online) have different requirements: it expects a quantitative foundation, although a computer science background isn’t required, and relevant professional experience is considered.
Is experimentation encouraged?
Experimentation is central to how Goldsmiths teaches AI, with the Creative Practice MSc structured around studio-style project sprints where students rapidly prototype and refine ideas in a collaborative cohort environment. Students also present original AI-driven work, such as films, interactive installations, or multimedia artworks, at a public symposium attended by industry partners.